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001			85265
003			ES-MaUEC
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007			cr nn 008mamaa
008			151221s2016 gw | s |||| 0|eng d
020			_a9783319269771
040			_aES-MaUEC
050		4	_aQA166.6 _b.C537 2016 EB
100	1		_aClark, Andrew _0Local _998092
245	1	0	_aSubmodularity in Dynamics and Control of Networked Systems _cby Andrew Clark, Basel Alomair, Linda Bushnell, Radha Poovendran
250			_a1st ed.
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XVII, 210 p.) _b63 ilustraciones, 15 ilustraciones col.
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	1		_aCommunications and Control Engineering _x0178-5354
520			_aThis book presents a framework for the control of networked systems utilizing submodular optimization techniques. The main focus is on selecting input nodes for the control of networked systems, an inherently discrete optimization problem with applications in power system stability, social influence dynamics, and the control of vehicle formations. The first part of the book is devoted to background information on submodular functions, matroids, and submodular optimization, and presents algorithms for distributed submodular optimization that are scalable to large networked systems. In turn, the second part develops a unifying submodular optimization approach to controlling networked systems based on multiple performance and controllability criteria. Techniques are introduced for selecting input nodes to ensure smooth convergence, synchronization, and robustness to environmental and adversarial noise. Submodular optimization is the first unifying approach towards guaranteeing both performance and controllability with provable optimality bounds in static as well as time-varying networks. Throughout the text, the submodular framework is illustrated with the help of numerical examples and application-based case studies in biological, energy and vehicular systems. The book effectively combines two areas of growing interest, and will be especially useful for researchers in control theory, applied mathematics, networking or machine learning with experience in submodular optimization but who are less familiar with the problems and tools available for networked systems (or vice versa). It will also benefit graduate students, offering consistent terminology and notation that greatly reduces the initial effort associated with beginning a course of study in a new area.
942			_2lcc _cLE
988			_aEBOOK, EBSPRINGER
650		7	_aAnálisis combinatorio _0comprobar BNE19900959889 _2embne _9138441
700	1		_aAlomair, Basel _0Local _998093
700	1		_aBushnell, Linda _0Local _998094
700	1		_aPoovendran, Radha. _998095 _0Local
830		0	_aCommunications and Control Engineering _x0178-5354 _9134209 _0Local
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-26977-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319269771
907			_a.b12945432 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b22-03-17 _cm _dz _ei _feng _ggw _h0
945			_aQA166.6 .C537 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11589322 _z06-04-17
999			_c85265 _d85265 _x1